ASSESSING ACCESSIBILITY: THE EFFECTS

OF ACCESSIBILITY IMPROVEMENTS ON

NEIGHBOURHOOD DEVELOPMENT

SUPERVISED RESEARCH PROJECT | ROBBIN DEBOOSERE

Assessing accessibility: The effects of accessibility improvements on

neighbourhood development

Prepared by Robbin Deboosere

Supervised by Ahmed El-Geneidy

School of Urban Planning McGill University

Montréal, Québec, Canada

April 2018

Supervised research project Submitted in partial fulfillment of the requirements of the degree of Master of Urban Planning

ASSESSING ACCESSIBILITY: THE EFFECTS OF ACCESSIBILITY IMPROVEMENTS ON NEIGHBOURHOOD DEVELOPMENT

Transport and land use are inextricably linked. Growth occurs where transport infrastructure is

constructed, and transport investments are made where land use is changing. Yet current planning documents tend to isolate transport by solely focusing on mobility, or how fast we can travel, while disregarding the destinations we want to reach.

To combat this flawed planning paradigm, accessibility indicators are increasingly being used in both research and practice. Accessibility improves upon the concept of mobility by also considering potential and desirable destinations, instead of only examining our ability to move through the transport network. The most common accessibility indicator measures the number of jobs that can be reached within a certain time threshold. Accessibility thus recognizes that most of us travel not for its own sake, but to reach a destination.

While cities such as London, Paris, and Sydney are now employing the concept in their transport plans, Canadian cities are lagging far behind and are missing out on the myriad of benefits unlocked by accessibility planning. To quantify these benefits, this study has examined both the social and economic effects of accessibility improvements on neighbourhood development.

Neighbourhood benefits of accessibility

Apart from the direct transport benefits, policies aimed at improving accessibility can help Canada’s neighbourhoods in the following five ways:

1. Increases in accessibility are related to increases in household income. The most vulnerable census tracts in Toronto where accessibility improved saw their income increase by $3,000 more than similar areas where accessibility remained stable.†

2. Accessibility improvements are associated with relative decreases in unemployment rates. The most vulnerable census tracts in Toronto where accessibility improved saw their unemployment rate rise by 1% less than similar areas where accessibility remained stable.†

3. Higher accessibility to jobs results in shorter commutes. If the average accessibility level in Toronto would be doubled, commute times would decrease by 2.12 minutes.

4. High accessibility locations attract more residential, commercial, and industrial development. If average accessibility to workers in Toronto would be doubled, each neighbourhood would attract 44 extra jobs per square kilometer.

5. Transit modal share is higher in high accessibility neighbourhoods. For every 100,000 jobs accessible by public transport, transit modal share increases by 4.25% (up to 6.21% in socially vulnerable areas).

What should policy makers do?

Here is how Canadian cities can seize the opportunity to be the world leaders in innovative accessibility planning:

1. Incorporate accessibility into transport planning documents. By using explicit accessibility objectives to reach planning goals, each decision will be based on a comprehensive understanding of the transport and land use interaction.

2. Evaluate transport investments through accessibility. Consider which areas will see their accessibility levels improve from the investment, and thereby also their income, unemployment rates, commutes, economic development, and transit mode share. Planners can thus use accessibility to foster socially inclusive environments that are also conducive to development.

3. Monitor progress of key planning goals and objectives through accessibility indicators.

Through these recommendations, Canadian cities can move away from the outdated mobility planning paradigm and instead utilize accessibility-oriented development techniques, and thereby maximize the benefits resulting from their transport investments.

† For every one unit increase in a competitive accessibility metric, which takes into account competition for jobs

III

ACKNOWLEDGEMENTS

I would first like to thank Ahmed El-Geneidy, my supervisor for this project, for his guidance and

support throughout my research and my Master’s degree. I would also like to thank my second

reader, Geneviève Boisjoly, for her thoughtful feedback and her contributions as co-author of the

second chapter of this supervised research project.

I would also like to thank David Levinson for his contributions as co-author of the third chapter of

this supervised research project. A thank you is also in order to David King, Nicholas Day and

Joshua Engel-Yan from Metrolinx for providing access to their data.

Finally, I would like to express my thanks to the Social Sciences and Humanities Research Council

of Canada and the Natural Sciences and Engineering Research Council of Canada for funding

this research.

IV

TABLE OF CONTENTS

Introduction 1

Chapter 1: Evaluating accessibility to low-income jobs across Canada 4

1. Literature review 6

2. Data and methodology 9

3. Results and Discussion 11

4. Conclusion 18

Chapter 2: Understanding the relationship between changes in accessibility

to jobs, income and unemployment in Toronto, Canada 20

1. Equity of accessibility and equity of outcome 21

1.1 Accessibility 21

1.2 Equity of accessibility 22

1.3 Accessibility, unemployment and income 23

2. Data and methodology 24

2.1 Study context 24

2.2 Data 25

2.3 Methodology 26

3. Results and discussion 27

3.1 Vertical equity 31

3.2 Linear regression models 32

4. Conclusion 35

Chapter 3: Accessibility-oriented development 37

1. Literature review 39

1.1 Accessibility 39

1.2 Transport, accessibility and economic development 40

2. Hypotheses, data, and methodology 42

2.1 Hypotheses 42

2.2 Case study 44

2.3 Data and methods 45

3. Results and discussion 48

3.1 Accessibility, commute duration and urban development 50

4. Conclusion 54

Conclusion 56

V

LIST OF FIGURES

Figure 1 - Overview of the 11 metropolitan areas included in the study 6

Figure 2 - Accessibility to all jobs in Toronto, Montreal and Vancouver 14

Figure 3 - Accessibility to low-income jobs in Toronto, Montreal and Vancouver 15

Figure 4 - Comparison of accessibility to all jobs and accessibility to low-income jobs 16

Figure 5 - Accessibility and public transport mode share 18

Figure 6 - Context map 26

Figure 7 - Median household income and unemployment rate in the GTHA in 2001 and 2011 29

Figure 8 - Transit accessibility in the GTHA in 2001 and 2011 30

Figure 9 - Relative competitive accessibility by transit, by income decile in the GTHA 31

Figure 10 - Context map of the Greater Toronto and Hamilton Area 45

Figure 11 - Accessibility to jobs and the labour force by car within 30 minutes 49

Figure 12 - Accessibility to jobs and the labour force by public transport within 45 minutes 50

LIST OF TABLES

Table 1 - Summary statistics for the 11 metropolitan areas 13

Table 2 - Summary statistics 27

Table 3 - Regression results for census tract median household income and unemployment rate in

2011 in the Greater Toronto and Hamilton area 34

Table 4 - Predicted 2011 income and unemployment rates for each income decile in 2001 35

Table 5 - Summary of AOD hypotheses 43

Table 6 - Descriptive statistics for commute duration, accessibility and development data 47

Table 7 - Regression model predicting average commute duration in 2001 51

Table 8 - Commute time and open area in 2011 fitted to accessibility in 2001 and changes in

accessibility between 2001 and 2011 52

Table 9 - Job and population density in 2011 fitted to accessibility in 2001 and changes in

accessibility between 2001 and 2011 54

1

INTRODUCTION

“It is clear that transport is a main factor in all large-scale town-planning. The arrangements of houses

must always have a direct relation to the daily movements of the inhabitants. […] Town-building and

transport are two elements in one problem that ought never to be separated. […] There can be no

good regional planning in London or anywhere else unless the location and the movement of

population are treated as one problem.” (The Spectator, 1933)

The concept of integrated land use and transport planning was not a new idea. In 19th century

London, urban thinkers had already recognized the inherent connection between transport and

land use. In 1846, Charles Pearson advocated the joint development of both railways and housing

estates, while other reformers in Victorian London recognized similar opportunities and

encouraged the construction of railways to disperse London’s ever-growing population (Barker &

Robbins, 1963; Dyos, 1955; Haywood, 1997). Even in the unregulated, laissez-faire climate

enjoyed by railways at the time, residential growth occurred where railways were constructed;

similar mechanisms were at play in the railroad towns of the North American hinterland, the

expansion of Berlin, or Boston’s streetcar suburbs (Ahlfeldt & Wendland, 2011; Sultana, 2017;

Warner, 1962).

Similar calls to action, advocating an integrated view on land use and transport planning, were

issued en masse in the 1960s (see, for example, W. Owen (1966) and Mumford (1968)). Yet

mobility planning, which had gained traction by traffic engineers in the 1950s and 1960s due to

the dominance of the private automobile, was the prevailing paradigm at the time: transport was

isolated from land use (P. W. G. Newman & Kenworthy, 1996). Endless suburban sprawl resulted.

In the last few decades, planners, increasingly concerned with traffic congestion, sprawl and

environmental degradation, have slowly started to shift away from the concept of mobility planning

towards accessibility planning – a shift accelerated by Calthorpe’s transit-oriented developments

and the New Urbanism movement (Calthorpe, 1993; Cervero, 1997; Congress for the New

Urbanism, 1999; P. W. G. Newman & Kenworthy, 1996). The accessibility planning paradigm

2

intends to revalorize the land use and transport connection, while re-focusing planning on people

and away from the automobile (Cervero, 1997).

Accessibility, or the ease of reaching destinations, is now increasingly being used in both research

and practice as a key performance indicator for the accessibility planning paradigm (Boisjoly & El-

Geneidy, 2017b; D. G. Proffitt et al., 2017). Accessibility, usually operationalized as the number

of opportunities (such as jobs or schools) an individual can reach within a certain time threshold,

improves upon the concept of mobility by also considering potential and desirable destinations,

instead of only examining an individual’s ability to move through the transportation network. As

such, accessibility to destinations explicitly considers the connection between land use and

transportation (Geurs & van Wee, 2004; Handy & Niemeier, 1997; Hansen, 1959; Wickstrom,

1971).

Metropolitan transport plans, such as those in London, Paris, Sydney, and Atlanta, are now

increasingly employing the concept of accessibility, either as an independent goal or objective, or

as part of an environmental justice assessment (Atlanta Regional Commission, 2016; Conseil

régional d'Île‐de‐France, 2014; NSW Government, 2012; San Diego Association of Governments,

2011; Transport for London, 2006). Authors of these transport plans argue that increasing access

to opportunities can reduce social disadvantage while at the same time acting as a catalyst for

economic growth. The plan for Sydney, for example, mentions supporting regional development

by improving accessibility to jobs, while in London improved access to employment is used as a

proxy for social inclusion (NSW Government, 2012; Transport for London, 2006).

Yet despite these hypothesised benefits, planners across Canada have been slow to adopt the

concept. Montréal’s 2008 transportation plan mentions the term vaguely, but does not include it

as an objective or goal (Ville de Montréal, 2008). In Toronto, the 2008 Big Move issued by

Metrolinx only provides a crude indicator for accessibility to public transport, and not to potential

destinations (Metrolinx, 2008). Only the 2016 discussion paper includes accessibility to jobs,

services and major destinations as an objective, and provides accessibility maps (Metrolinx,

2016). In contrast, Vancouver’s 2014 transport plan does not even mention accessibility to

destinations (Mayor's Council on Regional Transportation, 2014).

The transport plan for Calgary incorporates accessibility to the transit network, accessibility to daily

needs and accessibility to major destinations, but defines the concept vaguely as the “ease of

access/egress to any location by walking, cycling, transit, and private vehicles, or for commercial

vehicles” (The City of Calgary, 2009). Edmonton’s transport plan mentions accessibility to

3

employment opportunities, but only in the context of complete communities (City of Edmonton,

2009). Similarly, the plan for Ottawa focuses only on access to transit stations and not to

destinations, although some transit priority investments are rationalized through accessibility to

employment (City of Ottawa, 2013). Thus, among major Canadian metropolitan areas, only

Toronto is in the process of adopting accessibility to destinations as a key performance indicator.

In particular, Metrolinx aims to improve access to employment and other opportunities for

vulnerable populations, mirroring similar policies in London and Sydney aimed at creating

economic growth and reducing inequality.

To increase the uptake of accessibility planning among Canadian cities, the benefits of such an

approach should be more carefully examined: what can cities gain from adopting accessibility

planning and indicators? What could be the benefits of policies aimed at improving accessibility

levels? Only when planners are aware of these effects can they start to adopt the concept of

accessibility to guide planning policies in their city.

However, while a large body of literature has assessed accessibility levels for different cities,

socio-economic groups and neighbourhoods, or changes in these accessibility levels over time,

little research has been conducted to assess the outcomes of improvements in accessibility: what

will occur to neighbourhoods of different socio-economic status when accessibility levels change?

Can government policies, such as those in Sydney, London and Toronto, targeting accessibility

improvements in socially deprived neighbourhoods, help residents living in these areas? And can

accessibility improvements provide a catalyst for economic growth?

This supervised research project examines the impacts of changes in accessibility levels on

neighbourhood development. The first chapter provides a comprehensive look at the state of

accessibility in Canada’s largest cities and compares these cities in terms of the equity of their

transport and land use systems. In the second chapter, the social benefits of accessibility

changes are investigated, while the third and final chapter presents a study on the economic

benefits occurring from changes in accessibility levels. Both chapter two and three focus on the

context of the Greater Toronto and Hamilton Area, Canada’s largest metropolis, housing around

seven million inhabitants. Social benefits are measured through changes in neighbourhood

median income and unemployment rates, while economic benefits are measured through

residential and job density.

4

CHAPTER 1: EVALUATING ACCESSIBILITY TO LOW-INCOME JOBS ACROSS CANADA1

Transport and land use planning are inextricably linked. Although the modern concept of

integrated transport and land use planning goes back to at least the 1930s (one could even argue

as far back as 1846 when Charles Pearson suggested coupling housing estates and railway

stations in London), interest seems to have been renewed after Calthorpe’s proposal advocating

transit-oriented developments and the rise of the New Urbanism movement (Barker & Robbins,

1963; Calthorpe, 1993; The Spectator, 1933). Since, transport planners have started to move

away from the dominant mobility planning paradigm, which had gained traction by traffic engineers

in the 1950s and 1960s due to the dominance of the private automobile, towards the concept of

accessibility planning (D. G. Proffitt et al., 2017).

Accessibility, or the ease of reaching destinations, is now being increasingly used to operationalize

notions of integrated land use and transport planning and to act as a performance indicator for

these concepts (Boisjoly & El-Geneidy, 2017b). Indeed, metropolitan transportation plans, such

as those in London, Paris, Sydney and Atlanta, are now employing the concept, either as an

independent goal or objective, or as part of an environmental justice assessment (Atlanta Regional

Commission, 2016; Conseil régional d'Île‐de‐France, 2014; NSW Government, 2012; Transport

for London, 2006).

Starting with Hansen (1959) (and somewhat later Wickstrom (1971), Ingram (1971) and Dalvi and

Martin (1976), among others), a growing body of literature has been continuously refining the

concept of accessibility. By far the most widely used metric for accessibility is the number of jobs

an individual can reach within a set time limit (known as a cumulative opportunity measure). One

of the inherent strengths of this measure is that it makes comparisons between different socio-

1 Co-authored with Ahmed El-Geneidy

5

economic groups fairly straightforward. It is thus not surprising that accessibility, especially in

literature focusing on equity concerns, has often been used to identify which groups are being well

served, and which are being underserved, or to predict who will benefit from a new transport or

land use project. Contemporary research, however, relies on accessibility to jobs metrics that lack

the ability to accurately discern between distinct populations; all jobs are usually counted as being

accessible for everyone, as long as they can be reached.

This study refines the accessibility to jobs concept by developing a measure of accessibility to

low-income jobs for vulnerable residents by public transport. Such a metric has the advantage of

specifically considering the opportunities that can be accessed by vulnerable groups, instead of

grouping together e.g. finance and manufacturing jobs and assuming that these can be filled by

everyone. Furthermore, the metric employs realized public transport travel times by low-income

groups to correctly reflect the different characteristics between mode choice and commutes by

different socio-economic populations. The finer granularity of this metric allows planners and urban

decisionmakers to propose more targeted interventions. The differences between this more

refined metric and the regular accessibility measure are demonstrated by comparing eleven

metropolitan areas across Canada (see figure 1 for an overview of the cities included in this study).

As such, this study is the first to provide a comprehensive look at the state of accessibility in

Canada’s largest cities, and to compare these cities in terms of the equity of their transport and

land use systems.

The rest of this chapter is structured as follows. Section 2 provides a literature study on

accessibility metrics and the relationship between equity and accessibility. In section 3, the data

and methodology used to calculate accessibility to both all jobs and low-income jobs are

explained. Section 4 describes and discusses the results of the accessibility comparisons between

the eleven metropolitan areas, and section 5 concludes this chapter.

6

FIGURE 1 - Overview of the 11 metropolitan areas included in the study

1. LITERATURE REVIEW

Accessibility, or the ease of reaching destinations, is a comprehensive metric measuring the

interaction between land use and transportation (El-Geneidy & Levinson, 2006; Handy & Niemeier,

1997). The concept was first defined by Hansen (1959) (p.73) as “the potential of opportunities for

interaction”, and provides a measure for the variety and number of opportunities, or destinations,

that can be reached from a certain point in space through the transportation network. As such,

accessibility improves upon the concept of mobility by also considering potential and desirable

destinations, instead of only examining an individual’s ability to move.

Accessibility is generally understood to comprise four main components: land use, transportation,

time, and the individual (Geurs & van Wee, 2004). Place-based accessibility metrics measure

accessibility at a certain point in space, and usually only incorporate land use and transport factors

due to data limitations. Person-based metrics, on the other hand, founded in space-time

geography, focus on the individual, and thus also incorporate e.g. time budgets and socio-

economic information (Geurs & van Wee, 2004).

7

Three common metrics of place-based accessibility exist. Cumulative opportunity measures of

accessibility count how many destinations can be reached from a certain point in space within a

certain time threshold using a certain mode, e.g. the number of grocery stores an individual can

reach in 30 minutes by public transportation (Ingram, 1971; Morris et al., 1979; Wickstrom, 1971).

Gravity-based accessibility measures, on the other hand, discount these destinations or

opportunities by distance; the further an opportunity is, the less it contributes to accessibility

(Hansen, 1959; Koenig, 1980). Such measures thus relax the assumption made by cumulative

metrics that individuals stop travelling after a certain time threshold is reached, but are therefore

more difficult to compute and communicate to varying audiences, reducing their chances to impact

policy (Handy & Niemeier, 1997). A third set of commonly used measures are the utility-based

accessibility metrics, which assign each destination with a specific utility and calculate the logsum

of all destinations within a potential choice set (Handy & Niemeier, 1997). Utility-based

accessibility can thus be computed as the denominator of the multinomial logit model. While such

measures require extensive data collection, they can be converted into monetary units using

Hicksian compensation variation, rendering them easier to communicate to urban decisionmakers

(Geurs & van Wee, 2004; Niemeier, 1997).

Accessibility measures were developed with the intention of evaluating transport and land use

systems, while simultaneously allowing for the ability to measure the effects of proposed plans

and investment strategies (Morris et al., 1979). As such, accessibility is designed to accurately

measure the benefits resulting from interacting land use and transport systems. Such benefits

range from increased development potential in highly accessible locations (Ozbay et al., 2003), to

land value premia generated by accessibility (Martínez & Viegas, 2009), decreased risks of social

exclusion (Lucas, 2012), shorter unemployment duration for individuals experiencing high

accessibility levels (Andersson et al., 2014; Korsu & Wenglenski, 2010), and lower unemployment

rates among low-income households (Hu, 2017). Furthermore, accessibility has been linked with

shorter commutes (Levinson, 1998) and, in areas with high accessibility via public transport, higher

incidences of public transport use (A. Owen & Levinson, 2015b).

Accessibility is therefore often used to differentiate the effects of new transport plans between

socio-economic groups: who stands to benefit, and who will lose (Levinson, 2014; Manaugh & El-

Geneidy, 2012)? Driven by such notions of equity, transport researchers have developed a vast

amount of literature discussing accessibility and equity (see, among others, Bocarejo and Oviedo

(2012); Delbosc and Currie (2011); Delmelle and Casas (2012); El-Geneidy, Levinson, et al.

(2016); Foth et al. (2013); Guzman et al. (2017); Lucas et al. (2016); van Wee and Geurs (2011)).

8

Two conflicting notions of equity exist, however. To achieve horizontal equity, accessibility should

be equally divided across the entire population, regardless of individuals’ socio-economic status.

Vertical equity, on the other hand, would be achieved when those with the highest need

experience the highest accessibility; the concept thus posits that socio-economically vulnerable

populations should have higher accessibility (Karner & Niemeier, 2013).

While most scholars focus on measuring accessibility to jobs, accessibility has also been

calculated to health care facilities, schools, grocery stores, and a myriad of other opportunities

(Bissonnette et al., 2012; Grengs, 2015; Guagliardo, 2004; Luo & Wang, 2003; Mao & Nekorchuk,

2013). However, jobs remain the most prominent non-home destinations, and are thus particularly

useful in measuring an area’s attractiveness (A. Owen et al., 2016). Accessibility to jobs, besides

solely giving an indication of how many jobs an individual might reach, also has the added benefit

of providing a measure of nearby amenities; all services we’d like to reach, be they restaurants or

the theatre, for example, employ a certain amount of people and are therefore measured within

the accessibility to jobs framework. While some studies have examined and compared

accessibility to jobs across different cities (A. Owen et al., 2016; Ramsey & Bell, 2014; Tomer et

al., 2011), no such research has been undertaken in the Canadian context.

Despite the prominence of equity in accessibility research, there has been limited focus on

specifically measuring accessibility to low-income or low-wage jobs. Such accessibility metrics

provide a better representation of the state of access for vulnerable groups, as there are often

non-spatial barriers to acquiring high-wage employment (Legrain et al., 2016). While most studies

measuring accessibility to jobs often find that vulnerable groups experience higher accessibility

levels (see for example Foth et al. (2013)), these studies usually do not specifically measure

access to low-income jobs, i.e. to destinations that are more valuable for socially vulnerable

populations (Legrain et al., 2016).

This study contributes to the growing body of literature on accessibility to jobs in three major ways.

Firstly, we move away from the use of arbitrary time limits usually used in cumulative measures

of accessibility, and instead compute the average commute (once for all commuters, and once for

low-income commuters) to determine the time threshold, thereby more accurately modelling

people’s activity spheres. Secondly, along with calculating accessibility to all jobs, we also discern

accessibility to low-income jobs, and specifically measure the latter for vulnerable groups. Finally,

this paper is the first to compare such measures and the consequent equity of the transport and

land use interaction across eleven major Canadian metropolitan areas, in order to provide a more

holistic view on the equity of public transport provision in Canada.

9

2. DATA AND METHODOLOGY

To compute the accessibility to jobs metric, public transportation schedules across the eleven

cities were gathered in the General Transit Feed Specification (GTFS) format from their respective

transport agencies. Transit schedules were obtained for March 2017, or, when not available, for

the available dates closest to this date. For large metropolitan areas with multiple providers, the

schedules from all agencies were obtained with overlapping schedule dates.

Transit schedules were subsequently imported into a geographic information system through the

Add GTFS to a network dataset add-on for ArcGIS. A joint network between the public transport

network and the streets was then created. Travel times between census tract centroids were

computed based on the joint network, through a fastest route calculation during the morning peak

at 8 AM on a regular Tuesday. By creating a joint network, the algorithm would not force the fastest

path to solely choose public transportation: when a route between two census tracts was faster

by walking, the walking route was thus designated as the fastest. The computation for public

transport travel time incorporated walking time, waiting time, and in-vehicle time based on transit

schedules.

Jobs data was acquired through Statistics Canada, in the form of commute tables for each

Canadian province. The tables present the number of commuters, by personal total income and

mode of transport, working in each census tract; both commuters within the same census tract as

those outside are counted. Abstracting unfilled positions, the total amount of individuals working

in each census tract is an accurate proxy for the number of jobs in each tract.

To define the threshold for low-wage jobs, the incomes from the commuting tables were used.

While these might not necessarily perfectly reflect the wage obtained from a certain job (e.g. it

would overestimate a job’s wage for individuals with 2 or more jobs, as their total income would

be higher than any one of the two wages), the commuting tables provide a comprehensive and

uniform way to define a job’s wage across all Canadian provinces and metropolitan areas. To

reflect local variances in the cost of living and wage distribution, the low-income threshold was

determined individually for each metropolitan area as follows: the 30% lowest paying jobs in each

city were designated low-income. As income was only available in brackets, the closest bracket

was chosen as the threshold (e.g. if 29% of the jobs would fall within or below the $20,000-$30,000

bracket, then $30,000 would be the threshold).

Based on census tract travel times and jobs data, accessibility was computed via a cumulative

accessibility metric as follows:

10

�� = � ���

���� �� ���� = �1 � ��� ≥ ���������� 0 � ��� > ����������

where �� is the accessibility in census tract �, �� is the number of jobs in census tract � , ��� is the

travel time between census tracts � and �, and ���������� is the average commute by public

transportation in the region.

Accessibility to low-income jobs was calculated as follows:

�� � = ∑ �""∑ � "" � � ��

���� �� ���� = �1 � ��� ≥ ���������� 0 � ��� > ����������

where �� � is the accessibility to low-income jobs in census tract �, � � is the number of low-income

jobs in census tract � , ��� is the travel time between census tracts � and �, and ���������� is the

average commute by public transportation in the region for those travelling to low-income jobs. ∑ #$$∑ %&$$ represents the ratio of all jobs to low-income jobs in the metropolitan area (close to 10/3 per

definition), and is used to scale accessibility to low-income jobs so that it is directly comparable to

the accessibility to all jobs measure.

The average commute by public transportation in each metropolitan area was computed as

follows. The average travel time for all public transport commuters was calculated by multiplying

the number of individuals using public transport going from each census tract to each other census

tract by the travel time between these census tracts, resulting in total public transport travel time

in the region. This number was subsequently divided by the total number of public transport

commuters to obtain average travel time in the region. For low-income commuters, a similar

method was employed, but only low-income commuters were taken into account in the

calculations. Thus, for each metropolitan area, the average commute for all workers and the

average commute for low-income workers was computed separately. To calculate accessibility,

the average commutes were rounded up or down to the nearest multiple of 5 minutes.

To discern accessibility levels by socio-economic groups, a vulnerability indicator was estimated.

The indicator is composed of the following variables, based on the index for Canadian cities

developed by Foth et al. (2013):

• Median household income

• Unemployment rate

• The percentage of the population that has immigrated within the last 5 years

11

• The percentage of households that spend more than 30% of their total income on housing

rent

The final vulnerability indicator is given by the sum of the z-scores for the latter three variables,

minus the z-score for the median household income. A high indicator therefore reveals high

vulnerability. A census tract was subsequently designated as vulnerable if it was within the 30%

census tracts with the highest indicator.

3. RESULTS AND DISCUSSION

Table 1 presents summary statistics for the 11 metropolitan areas included in this study. Note that

Toronto-Hamilton refers to the entire Greater Toronto and Hamilton Area and thus includes the

Toronto, Hamilton, and Oshawa census metropolitan areas as defined by Statistics Canada. The

11 included areas represent the 10 largest cities in terms of metropolitan population, plus Halifax

to speak for Atlantic Canada. This subset of Canadian cities includes a wide variety of contexts,

from large megalopolises such as Toronto, to smaller regional centres such as London and

Halifax.

However, the size of a metropolitan area does not seem to be reflected in average commute times

– in most cities this figure hovers between 45 and 60 minutes, with Winnipeg residents

experiencing the shortest commutes. This might be explained by better public transport provision

(such as the presence of commuter rail, subways and elevated rail) in larger cities such as Toronto,

Montreal and Vancouver.

On average, it appears that low-income workers have shorter commutes. This difference is

especially profound in Edmonton, where low-income workers travel 10 minutes less than average,

while in London and Kitchener-Cambridge-Waterloo, low-wage commuters are forced to travel

longer than average, although the difference is not large. Thus, computing accessibility at the

same threshold for both low-income workers and all commuters would generally overestimate the

activity spheres of vulnerable populations and thereby result in overestimates of accessibility

levels. As these estimates could then feed into policy, they might result in biased

recommendations that could negatively affect low-income workers.

Note that, in London and Kitchener-Cambridge-Waterloo, low-income workers travel longer on

average, resulting in a higher threshold for the calculation of their accessibility. This raises

questions about choice: are low-income groups in these two cities indeed more willing to travel

longer, or are they travelling longer because their choices are constrained? Comparing with the 9

other cities in this study, the latter seems more plausible; this might indicate a limitation on the use

12

of realized travel times for the calculation of accessibility. Such an approach therefore requires

planners to be inimically knowledgeable about local circumstances to choose appropriate

accessibility indicators.

Figures 2 and 3 present a comparison between accessibility to all jobs and accessibility to low-

income jobs in Canada’s three largest cities: Toronto, Montreal and Vancouver. Average

accessibility levels appear to be related to a city’s size and its number of jobs. Torontonians

experience the largest accessibility levels on average (around 422,000 jobs in an average

commute), while Vancouver inhabitants can only reach 209,000 jobs in an average commute. The

average accessibility level in Montreal is 365,000 jobs.

Distinct patterns of access can be discerned from the maps: accessibility generally drops off with

increasing distance from the central business district, but along rapid transit lines – commuter rail

and the subway in Toronto and Montreal, or the Skytrain in Vancouver – accessibility levels, to

both low-income jobs and all jobs, remain higher, reflecting the benefits conveyed by fast and

frequent modes of transport. While subways seem to generate a linear pattern of high access,

commuter rails only induce high access at stations, mirroring differences in stop spacing between

the two heavy-rail modes. Local employment centres, on the other hand, have a minor effect on

accessibility levels, as the jobs present in these areas are often dwarfed in number by those

located in the central business district, or because these centres were co-located with rapid transit

stops (such as at the Scarborough Centre Station in the Greater Toronto and Hamilton Area).

Interestingly, vulnerable Montreal residents are more fortunate than those in Toronto in terms of

accessibility to low-income jobs: vulnerable Montrealers can access around 133,000 low-income

jobs (449,000 scaled jobs), while vulnerable Torontonians can only reach 93,000 in an average

commute for low-income residents (312,000 scaled jobs), even though the number of low-income

jobs in Toronto is much higher.

13

TABLE 1 - Summary statistics for the 11 metropolitan areas

Metropolitan area P

opul

atio

n

Tot

al n

umbe

r of

jobs

Pop

ulat

ion

dens

ity

(pop

/km

2 )

Med

ian

hous

ehol

d in

com

e (C

AD

)

Une

mpl

oym

ent r

ate

(%)

Ave

rage

com

mut

e (m

in)

Ave

rage

com

mut

e th

resh

old

(min

)

Ave

rage

low

-inco

me

com

mut

e (m

in)

Ave

rage

low

-inco

me

com

mut

e th

resh

old

(min

)

Low

-inco

me

thre

shol

d (C

AD

)

Calgary 1,392,609 585,860 272.5 99,583 9.3 58.29 60 56.84 55 40,000

Edmonton 1,321,426 551,140 140.0 94,447 8.5 63.95 65 53.62 55 40,000

Halifax 403,390 180,860 73.4 69,522 7.3 60.14 60 54.15 55 30,000

Kitchener - Cambridge - Waterloo

523,894 227,500 480.1 77,229 6.4 45.88 45 45.90 46 30,000

London 494,069 199,090 185.6 64,743 7.3 49.23 50 51.42 50 30,000

Montreal 4,098,927 1,750,605 890.2 61,790 7.5 50.51 50 45.85 45 30,000

Ottawa - Gatineau 1,323,783 594,745 195.6 82,053 7.0 54.93 55 49.99 50 40,000

Quebec 800,296 374,680 234.8 65,359 4.6 50.26 50 46.37 45 30,000

Toronto - Hamilton† 7,055,433 2,935,930 862.4 78,437 7.6 58.37 60 52.41 50 30,000

Vancouver 2,463,431 1,004,375 854.6 72,662 5.8 49.86 50 47.41 45 30,000

Winnipeg 778,489 343,365 147 70,795 6.3 43.00 45 42.38 40 30,000 † Refers to the Greater Toronto and Hamilton Area, and includes the Toronto, Hamilton, and Oshawa census metropolitan areas

14

FIGURE 2 - Accessibility to all jobs in Toronto, Montreal and Vancouver

15

FIGURE 3 - Accessibility to low-income jobs in Toronto, Montreal and Vancouver

16

FIGURE 4 - Comparison of accessibility to all jobs and accessibility to low-income jobs

In figure 4, a comparison between average accessibility levels, to all jobs and to low-income jobs,

for all cities included in this study can be seen, which again confirms the difference in access

between Toronto and Montreal as noted above. The blue dots represent the commonly used

accessibility to all jobs metric, while the orange dots show the average of the former metric in

socially vulnerable neighbourhoods. The red dots finally represent the new metric of accessibility

to low-income jobs, averaged for socially vulnerable neighbourhoods.

A city’s size does not fully predict average accessibility levels; in Vancouver, for example, an

average resident can only access as much opportunities as in Edmonton, Ottawa, or Calgary,

which are home to around 700,000 less people and 400,000 less jobs. Similarly, inhabitants of

Kitchener-Cambridge-Waterloo and London have lower accessibility than those in Halifax, even

though the latter city houses 100,000 less residents. The explanation for such discrepancies are

to be found in the particular allocation of land uses and the speed, frequency and coverage of the

transport systems present in these cities.

Note the large differences between the accessibility to all jobs for vulnerable groups (orange) and

accessibility to low-income jobs for vulnerable groups (red). These discrepancies can be explained

by the difference in spatial allocation and distribution of low-income jobs versus all jobs, and by

17

the different activity spheres and thus travel times realized by vulnerable groups. In all cities

(except for Kitchener – Cambridge – Waterloo), accessibility for vulnerable individuals would have

been overestimated with the commonly used all jobs metric. In Toronto, for example, policy

makers only having access to an all jobs metric would conclude that vulnerable groups are better

off than other residents. However, focusing only on low-income jobs that can be accessed by

these individuals, it is clear that this is not the case.

In all cities, except for Toronto, Calgary, Edmonton, and Quebec, vulnerable groups still have

access to a larger number of relevant job opportunities than average; vertical equity thus appears

high in most Canadian metropolitan areas. Nevertheless, the variation between accessibility to all

jobs and accessibility to low-income jobs for vulnerable groups (especially in Toronto and

Montreal) highlights the benefits of calculating these two metrics separately.

To further illustrate the importance of distinguishing between the two accessibility metrics, we

plotted accessibility against transit modal share, for both the regular accessibility measure, and

the measure of accessibility to low-income jobs for vulnerable residents, see figure 5. Transit

accessibility has been shown to be correlated with public transport mode share (A. Owen &

Levinson, 2015b), and as such the effects of access on mode share provide a representative

example demonstrating the significance of employing two separate accessibility metrics. The

difference between the two accessibility levels can again be distinguished – accessibility to low-

income jobs for vulnerable groups is, for most cities, higher than the regular accessibility metric.

Importantly, the effect of the accessibility metric on transit modal share differs between the two

measures. While for every increase of 100,000 jobs in the regular accessibility measure, modal

share increases by 4.25%, the comparable figure for the low-income accessibility metric for

vulnerable groups is 6.21%. Recall that low-income accessibility was scaled by the ratio of all jobs

to low-income jobs, and is therefore directly comparable to accessibility to all jobs; the contrast in

effect sizes is therefore not due to a lower number of low-income jobs. Thus, vulnerable

populations appear to be more sensitive to changes in accessibility to jobs they can access – had

decisions been based on the regular accessibility metric, they would have considerably

underestimated the effect of increasing accessibility to low-income jobs for vulnerable residents.

Future research should examine the effects of accessibility to low-income jobs in further detail.

18

4. CONCLUSION

Accurate policy recommendations require the creation of adequate and robust performance

indicators that can precisely measure progress. This study has therefore developed the metric of

accessibility to low-income jobs for vulnerable residents, which, in contrast with commonly used

measures of accessibility to all jobs, provides policy makers and urban planners with a more fine-

grained tool to examine the effects of new transportation plans and projects across different socio-

economic populations. In effect, this detailed accessibility measure provides a first step towards

the segmentation of different groups within accessibility literature and practice – a common

occurrence in studies on the different types of cyclists and public transport users (Damant-Sirois

et al., 2014; Jensen, 1999; Krizek & El-Geneidy, 2007). Through such a segmentation approach,

the benefits of both place-based and people-based accessibility metrics can be reconciled, namely

the communicability and data requirements of place-based measures, combined with the detail

common in people-based metrics.

This study has compared accessibility to all jobs by public transport, and the new metric of

accessibility to low-income jobs for vulnerable groups for 11 metropolitan areas in Canada,

ranging from large metropolises to smaller regional cities. On average, Toronto residents

experience the highest accessibility levels. When focusing on low-income jobs, however,

vulnerable groups are far better off in Montreal in terms of accessibility, even though the total

number of low-income jobs is much lower than in Toronto. Overall, it appears that vulnerable

individuals experience higher accessibility levels than their fellow residents in their respective

FIGURE 5 - Accessibility and public transport mode share

19

cities, although this trend does not appear in Toronto, Calgary, Edmonton, and Quebec; these

latter cities thus seem to lag behind their counterparts in terms of vertical equity. The

discrepancies between the two metrics were further highlighted by comparing the effects of access

on public transport mode share: it is clear that vulnerable residents are more sensitive to

accessibility changes. Planners and urban decisionmakers, and in turn their policy

recommendations, thus stand to benefit greatly from employing more detailed and segmented

accessibility metrics.

20

CHAPTER 2: UNDERSTANDING THE RELATIONSHIP BETWEEN CHANGES IN ACCESSIBILITY TO JOBS, INCOME AND

UNEMPLOYMENT IN TORONTO, CANADA2

In many urban areas, transport agencies are trying to provide all citizens with greater access to

opportunities as a means to improve residents’ well-being (Boisjoly & El-Geneidy, 2017b; Handy,

2008; Proffitt et al., 2015). Several cities particularly intend to increase access to opportunities in

socially deprived areas, in order to support social inclusion and enhance the quality of life of

residents in these neighbourhoods (NSW Government, 2012; San Diego Association of

Governments, 2011; Transport for London, 2006). In this context, research suggests that

improvements in access to opportunities by public transport can bring considerable benefits to

vulnerable populations, as they are more likely to rely on this mode for accessing their destinations

(Stanley & Lucas, 2008).

To quantify access to opportunities, accessibility, or the ease of reaching destinations, is

increasingly being used in research and practice as a key land use and transportation performance

measure. From a social equity perspective, accessibility has been used as a tool to assess the

socio-spatial distribution of public transport services (Bocarejo & Oviedo, 2012; Delmelle & Casas,

2012; Golub & Martens, 2014; Kawabata & Shen, 2007), and to evaluate how changes in

accessibility differ across socio-economic groups as a result of projected or new infrastructure

projects (Foth et al., 2013; Manaugh & El-Geneidy, 2012; North Central Texas Council of

Governments, 2016; Paez et al., 2010; Southern California Association of Governments, 2016).

While a large body of literature has assessed accessibility levels for different socio-economic

2 Co-authored with Geneviève Boisjoly and Ahmed El-Geneidy. Paper presented at the 2018 Annual TRB Meeting, Washington DC.

21

groups, or changes in these accessibility levels over time, little research has been conducted to

assess the outcomes of such improvements in accessibility.

The goal of this study is, therefore, to assess the relationship between improvements in the levels

of accessibility to jobs by public transport and the resulting socio-economic benefits, measured by

changes in median household income and unemployment rate over time in the Greater Toronto

and Hamilton Area, Canada. For this purpose, competitive accessibility levels to employment

opportunities by transit and by car are calculated for all census tracts in 2001 and 2011. The

vertical equity of accessibility by transit is then assessed for both years by comparing accessibility

levels across median household income deciles. Two linear regressions are subsequently

performed to examine the relationship between accessibility changes and income and

unemployment at the census tract level, while controlling for the movement of residents. This study

contributes to the literature on accessibility and the equity of outcome resulting from these

accessibility levels, and is of relevance to planning professionals and researchers wishing to

investigate the effects of accessibility improvements across neighbourhoods, especially low-

income ones.

The rest of the chapter is organised as follows. Section 2 explains the concept of accessibility,

examines how equity is incorporated in academic literature on this concept, and presents previous

literature on accessibility, employment and income. Section 3 considers the data and methodology

used to investigate the relationship between improvements in transit accessibility and changes in

income and unemployment, and section 4 presents and discusses the findings. Section 5 then

concludes the chapter and provides recommendations for further research.

1. EQUITY OF ACCESSIBILITY AND EQUITY OF OUTCOME

1.1 Accessibility

Accessibility was first defined by Hansen (1959) (p.73) as “the potential of opportunities for

interaction”. In contrast with mobility, accessibility also considers land use factors such as the

variety and number of destinations that can be reached, instead of only examining an individual's

ability to move through the transportation network (Handy & Niemeier, 1997). Geurs and van Wee

(2004) posit that accessibility measures should comprise four interacting components: land use,

transportation, time, and the individual. Accessibility thus tries to incorporate the spatial distribution

of activities, the transport system connecting these activities, the time constraints of individuals

and services, and personal needs and abilities to provide a more accurate picture of the

performance of transport systems.

22

There are several commonly used measures of accessibility, most of which take into account only

the land use and transportation component, as they can be more easily computed, interpreted,

and communicated, increasing their chances to impact policy (Geurs & van Wee, 2004; Handy &

Niemeier, 1997). Cumulative measures of accessibility count the number of opportunities that can

be reached within a set time-frame, for example the number of jobs an individual can reach within

45 minutes of travel (Wickstrom, 1971). Gravity-based accessibility measures, on the other hand,

take into account that people will not stop travelling at an arbitrary time-limit, and weigh

opportunities by distance; the further an opportunity is, the less it contributes to accessibility

(Hansen, 1959). While more realistic, gravity-based measures require the prediction of a distance

decay function, rendering them more difficult to communicate, interpret and analyze across

studies.

To account for competition effects, for example among workers competing for jobs, the concept

of accessibility has also been extended to include measures of competitive accessibility (Q. Shen,

1998). As cumulative and gravity-based accessibility only measure the ‘supply side’ of

opportunities (Geurs & van Wee, 2004; Morris et al., 1979), they assume that no capacity

limitations exist. Therefore, when accessibility to jobs is examined through the lens of ordinary

cumulative or gravity-based accessibility measures, it is assumed that one job can be filled by an

infinite number of workers. To more accurately reflect reality, a demand potential is first computed

by determining how many individuals can access each opportunity. Each opportunity is then

discounted by this demand potential when calculating accessibility using the cumulative or gravity-

based approach in what is known as a competitive measure of accessibility (Q. Shen, 1998).

1.2 Equity of accessibility

Measures of accessibility have often been used to consider the equity of the joint benefits provided

by the land use and transportation system (see for example (Delmelle & Casas, 2012; Golub &

Martens, 2014; Grengs, 2015; Guzman et al., 2017)). Two different interpretations of equity in

accessibility research exist, both founded in the ethical concept of egalitarianism (Foth et al., 2013;

van Wee & Geurs, 2011). Horizontal equity requires that all members of society have equal access

to all resources. Vertical equity, on the other hand, implies that the more vulnerable groups should

be granted more resources. From this point of view, it would be more beneficial to society to

increase the accessibility of unemployed young individuals than to increase the accessibility of

wealthier individuals (Lucas et al., 2016). Yet another approach defines an equitable system as

having a minimal gap between transit and car accessibility (Golub & Martens, 2014; Karner &

23

Niemeier, 2013), after which both the horizontal and vertical equity of the distribution of this gap

can be measured.

Current literature mostly focuses on examining the vertical equity impacts of transportation

projects. To examine this type of equity, socially vulnerable groups first need to be defined.

Several studies identify socio-economic groups based solely on income (for example (Fan et al.,

2012; Guzman et al., 2017)), whereas other studies also examine race, poverty status, minorities,

and housing characteristics (Delmelle & Casas, 2012; Golub & Martens, 2014; Grengs, 2015), or

create a social indicator combining several of these measures (Foth et al., 2013). The vertical

equity of accessibility can then be investigated by comparing accessibility levels across different

populations.

A distinction is often made between equity of opportunity and equity of outcome (Delbosc & Currie,

2011; Litman, 2002; van Wee & Geurs, 2011). Studies discussing the horizontal and vertical equity

of accessibility address equity of opportunity, but refrain from making judgements on the outcome

of the process. This paper attempts to connect the two concepts by considering the link between

equity of opportunity, measured by accessibility, and equity of outcome, measured by changes in

unemployment and income over time.

1.3 Accessibility, unemployment and income

To determine the outcomes and subsequent benefits resulting from accessibility and accessibility

changes, previous studies have focused on examining the relationship between accessibility to

jobs and socio-economic status, mostly concentrating on unemployment duration. Korsu and

Wenglenski (2010), using micro-data, demonstrate that low accessibility to jobs is related to high

unemployment in Paris, and find that workers living in areas with very low accessibility have a

1.7% higher probability of being unemployed for longer than one year compared to workers living

in neighbourhoods with medium accessibility. To this end, the authors use a measure of

cumulative accessibility, by public transport or car depending on car ownership, specifically

considering the employment opportunities of the same socio-professional status as the individuals

in question. Andersson et al. (2014) investigate low-income workers who were subject to mass

layoffs in several US cities, and find that high accessibility to jobs is associated with a reduction

in the time spent looking for work. A competitive measure of accessibility to low-income jobs is

used for this purpose, taking into account the probability of using car or public transport, and

explicitly considering competing job searchers to account for labour market tightness. Tyndall

(2015) notes that after the closure of the R train in Brooklyn due to hurricane Sandy,

24

unemployment rates along the line increased considerably, especially for those without a private

vehicle, demonstrating that substantial changes in the public transport system affect

unemployment. This study did not, however, examine the accessibility impacts of this endogenous

shock to the transport system. Blumenberg and Pierce (2014) find that living close to a bus stop

highly increases the chances of maintaining consistent employment, while having access to a

private automobile has also been shown to be related to increased employment (Blumenberg &

Pierce, 2017). Larson (2017) examines the relationship between access to jobs by public transport

(broadly defined as the observed transit modal share) and economic opportunity over four

decades in four US cities, and concludes that there is a positive relation between transit access

and economic opportunity in predominantly white neighbourhoods in Orlando and Minneapolis,

while a similar relationship is present in non-white areas in Birmingham.

This emerging body of literature suggests that accessibility to jobs is a potential determinant of

unemployment duration. However, little is known about the relationship between unemployment

rates and accessibility over time at a more aggregate, metropolitan scale; the literature presented

above has not examined how accessibility changes impact longer term unemployment duration

and more aggregated unemployment rates. Furthermore, no study has, to our knowledge,

examined changes in accessibility and median household income over time. To provide a more

holistic view on the relationship between accessibility changes and consequent changes in socio-

economic status at an aggregate level, this study attempts to investigate the change in both the

unemployment rate and median household income over a ten-year period. This paper therefore

contributes to the literature by presenting a long-term study associating a robust accessibility

measure with equity of outcome.

2. DATA AND METHODOLOGY

2.1 Study context

The Greater Toronto and Hamilton Area, the most populous metropolitan region in Canada,

housing 5.6 million residents in 2001 and 6.6 million inhabitants in 2011, was chosen to examine

the relationship between transit accessibility improvements and changes in income and

unemployment. The region is well connected by public transport, and is home to a subway,

commuter train system and bus network (Figure 6). While the subway only serves the City of

Toronto, the bus and train network extend across the entire region. During the ten-year study

period, several infrastructure projects altered the public transport network in the area. In 2002, a

new subway line, the Sheppard line (the line shown in green in figure 6), was opened, serving five

25

new stations in the north of the City of Toronto. Additionally, several new train stations were

constructed and new express bus services were introduced. At the same time, transit mode share

increased from 20% in 2001 to 21% in 2011.

2.2 Data

Three different data sources were used for the analysis. Census and employment data for 2001

and 2011 were obtained from Statistics Canada. This data was enriched by a cumulative

accessibility measure for a 45-minute trip by transit in 2011 at the census tract level, derived from

GTFS data. The third data source, Metrolinx, provided travel time from 2001 at the traffic analysis

zone (TAZ) level, calculated through the EMME travel demand modelling software, for both public

transportation and automobile. Additionally, car travel time from 2011 during the AM peak was

also supplied by Metrolinx.

A competitive measure of accessibility for 2001 at the TAZ level was first calculated using 2001

travel times and employment. Competitive accessibility is given by:

�'� = ∑ #()(�+(,).(,� , where /�' = ∑ �0�� (���')

�'� reflects the accessibility at point i for transportation mode m, �� is the number of opportunities

at location j, and (���') is 1 when the travel time between locations i and j (���') is smaller than the

set-time limit, and 0 otherwise. /�' represents the demand for the opportunities at location j, and

is given by the total labour force (�0�) that can access those opportunities within the set time-limit.

To ensure consistency with available data from 2011, and to allow for comparisons, the

accessibility measure was calculated for a 45-minute trip limit for public transport, and a 30-minute

limit for car, and then projected into 2011 census tract boundaries through a nearest neighbour

interpolation. These time limits reflect the average commute times in Toronto for both modes (49

and 29 minutes respectively (Statistics Canada, 2010)), in order to capture the opportunities an

individual can access in an average trip, while accounting for competition from other residents

trying to reach the same opportunities.

26

FIGURE 6 - Context map

2.3 Methodology

To investigate the relationship between improvements in transit accessibility and changes in the

unemployment rate and median household income, two linear regression models are employed.

The first model predicts median household income in 2011, based on median household income

in 2001 and changes in accessibility by car and transit between the two years. The second model

is specified in a similar manner: the unemployment rate in 2011 is related to the unemployment

rate in 2001 and changes in accessibility levels.

As changes in income, especially for low income census tracts, could be related to gentrification,

i.e., the upgrading of the socio-economic status of a neighbourhood through local migration

(Lyons, 1996), several additional variables are added to the model. Literature on the relation

between transit and gentrification usually investigates land and housing values, changes in

income, race, car ownership, the number of professionals, and educational attainment to identify

27

gentrifying areas (Grube-Cavers & Patterson, 2015; Kahn, 2007; Pollack et al., 2011). A

neighbourhood is said to be gentrifying if these variables change faster than the average in the

metropolitan area. Such an approach, however, does not account for the movement of people.

Some of the changes noted by the literature could, instead of being linked to gentrification, have

resulted from an improvement in the conditions of the individuals living in a certain neighbourhood,

without the presence of outside forces pushing these residents out; increases in income do not

always imply that people were pushed out and wealthier individuals moved in (Freeman, 2005).

Also incorporating the percentage of people moving mitigates these disadvantages and

acknowledges that in-movers are the driving force behind gentrification (Freeman, 2005).

Consequently, the change in the percentage of residents with a bachelor’s degree or higher, and

the percentage of residents that have moved between 2006 and 2011 are included in the

regression model to control for the effects of gentrification, and, more broadly, migration. The

summary statistics of the variables used in the two models are shown in table 2.

TABLE 2 - Summary statistics

Variable Mean Standard dev.

Median Household Income in 2011 ($1,000) 75.664 26.536 Median Household Income in 2001 ($1,000) 64.534 21.558 Unemployment rate in 2011 (%) 8.7173 3.1598 Unemployment rate in 2001 (%) 5.7868 2.4814 Change in competitive accessibility by transit (jobs/worker) -0.0897 1.1893 Change in competitive accessibility by car (jobs/worker) 0.2422 0.2917 Change in percentage of residents with a bachelor’s degree or higher (%)

4.3710 4.9699

Percentage of residents that have moved between 2006 and 2011 (%)

35.131 11.480

3. RESULTS AND DISCUSSION

Figure 7 shows the spatial distribution of median household income and the unemployment rate

in the GTHA in 2001 and 2011. In the top two maps, the lightest colour represents the census

tracts with the lowest income, whereas the darkest color represents the least vulnerable

neighbourhoods. In both years, the low-income census tracts are centred in a ring around

downtown Toronto, although a suburbanization of low income areas has occurred; the

neighbourhoods to the north and east of the City of Toronto have become more vulnerable in

2011. The outer suburbs, as well as the CBD of Toronto, house higher income populations in both

years. In the bottom map, the lowest unemployment rate is presented in the lightest color, while

the highest unemployment rate is shown in the darkest color. The financial crisis of 2007-2008

28

radically changed the pattern of unemployment across the region: the unemployment rate

skyrocketed between 2001 and 2011 in almost every census tract, especially in the outer suburbs.

The spatial distribution of competitive accessibility by public transport and car in both 2001 and

2011 are shown in figure 8. Transit accessibility was calculated for a maximum travel time of 45

minutes, whereas car accessibility was computed for a 30-minute trip. The two modes display

profoundly different spatial patterns, due to significant directionality present in the public transport

system. During the morning peak, the GO train network focuses on bringing residents into the

Toronto CBD, while the service in the opposite direction is close to non-existent. Suburban job

centres are therefore protected from competition by transit: only local residents can access these

employment opportunities, resulting in high competitive accessibility levels. Competitive

accessibility by transit is thus mainly determined by competition effects. In contrast, accessibility

by car is mostly influenced by the presence of job opportunities, as directionality is less present in

the highway and street networks. Car accessibility is thus highest in downtown Toronto, where the

largest amount of job opportunities is present. Between 2001 and 2011, accessibility by private

automobile rose substantially in Toronto and in the western parts of the region, whereas a small

decrease was observed in the eastern census tracts. At the same time, competitive accessibility

by transit increased in a few clusters of suburban job centres, and decreased in the rest of the

Greater Toronto and Hamilton Area.

29

FIGURE 7 - Median household income and unemployment rate in the GTHA in 2001 and 2011

30

FIGURE 8 - Transit accessibility in the GTHA in 2001 and 2011

31

3.1 Vertical equity

Figure 9 presents transit accessibility standardized values (z-scores) by income decile. In 2001,

the four deciles with the lowest income in the region experience considerably higher competitive

accessibility levels by transit than all other groups, highlighting that accessibility is vertically

equitable in the GTHA, which is consistent with the findings of Foth et al. (2013) for the Greater

Toronto and Hamilton Area. Competitive accessibility of the four groups with the lowest income

decreased between the two years, however, although they continue to have a considerably higher

accessibility than the other income deciles. The investments in commuter trains, connecting

wealthier neighbourhoods to downtown Toronto, have therefore succeeded in increasing

accessibility to employment for high income census tracts. This suggests that, while the vertical

equity of the transportation and land use system is still high in the GTHA, there is a trend towards

decreasing vertical equity and increasing horizontal equity. Note that, as socially vulnerable

groups have lower car ownership (Potoglou & Kanaroglou, 2008), this decrease in accessibility

can result in substantial negative consequences for the region’s most vulnerable populations. To

quantify the effects of these accessibility changes on neighbourhood socio-economic status,

results of the linear regression models are presented in the next section.

FIGURE 9 - Relative competitive accessibility by transit, by income decile in the GTHA

32

3.2 Linear regression models

Table 3 shows the results of the two linear regression models, with both models showing similar

patterns. Only the variables that are statistically significant will be described here. The model

predicting median household income in 2011 demonstrates that higher median household income

in 2001 is associated with higher median household income in 2011, while the coefficient of 1.12

for this variable suggests that overall income levels rose by 12% during the study period, while

controlling for all other variables present in the model. Changes in competitive accessibility by

transit, and the interaction term between this variable and median household income in 2001, are

significantly related to income in 2011. For example, a census tract with a median household

income of $40,000 in 2001 is predicted to have an extra increase in income of (7.67 – 0.099*40)

= 3.71 ($3,710) in 2011 per extra unit in competitive accessibility (Table 3). A one unit increase in

competitive accessibility occurs when a person can access an extra job that is not accessible to

all other residents in the region. The effect of competitive accessibility reverses when income in

2001 is higher than $77,475. As higher income populations are more likely to move to less dense

areas in search for open space, they tend to migrate to areas without public transport access. As

a result, median income decreases in areas where these wealthy groups move out. Increases in

competitive accessibility by car are also statistically significant and associated with higher incomes

in 2011: a one unit increase in car accessibility is predicted to increase income by $3,370. An

interaction term between car accessibility and baseline household income in 2001 was also

analyzed, but was not significant, indicating that the effect of accessibility by car is income-

independent.

The remaining statistically significant coefficients highlight that increases in the percentage of

residents with a bachelor’s degree or higher, and stable neighbourhoods (without many people

moving) are related to higher median household incomes in 2011. The coefficients for accessibility

changes by both car and public transport highlight that changing equity of opportunity, measured

by accessibility, is associated with a changing equity of outcome, measured by income.

The second model indicates that higher unemployment rates in 2001 are associated with higher

unemployment rates in 2011, suggesting that census tracts with high unemployment rates in 2001

still have higher unemployment in 2011. An extra accessible job by transit that cannot be reached

by any other individual (a one unit increase in transit accessibility) is related to a 2.5 percentage

point decrease in unemployment rate for census tracts with a median household income of $0. If

median household income in 2001 increases, the effects of changes in transit accessibility lessen

and reverse at a median household income of $78,052. In contrast, the change in car accessibility

33

has a uniform effect across income: one extra accessible job by car that cannot be reached by

others is linked to a decrease of 0.54 percentage points in unemployment rate. As with the model

predicting income, increases in the percentage of residents with a bachelor’s degree or higher are

significantly associated with lower increases in the unemployment rate. These results are

consistent with the findings presented by Tyndall (2015), who found that a substantial change in

the provision of public transport (and thus a considerable change in access by transit) was

associated with changing unemployment. This suggests that the conclusions by Korsu and

Wenglenski (2010) and Andersson et al. (2014) can be extended from unemployment duration at

the individual level to aggregated unemployment rates at the neighbourhood scale.

Table 4 presents predicted values for median household income and the unemployment rate in

2011 for all income deciles in 2001. The values are predicted for a constant transit accessibility,

and for a transit accessibility that increased by one unit during the study period. Median household

income in 2011 is greater for all deciles except the two wealthiest groups if accessibility by public

transport increased instead of remaining constant. The premium generated by transit accessibility

ranges from $3,812 for the lowest income decile to -$13,744 for the highest income decile. A

similar pattern is present in the predicted unemployment rates: the predicted effect of a unit

increase in competitive accessibility by transit is -1.28 percentage points for the poorest census

tracts, and 4.52 percentage points for the wealthiest decile. Based on these predictions, we can

infer that the decreasing vertical equity of transit accessibility (as shown in figure 9) is associated

with a widening of the income gap in the GTHA.

34

TABLE 3 - Regression results for census tract median household income and unemployment rate in 2011 in the Greater Toronto and Hamilton area

Income Unemployment rate

Variable Coefficient Sig. Confidence

interval†

Coefficient Sig. Confidence

interval†

Constant 5.11 *** 2.071 8.15 4.7788 *** 4.2652 5.2925 Median household income in 2001 1.121 *** 1.093 1.149 - - - - Unemployment rate in 2001 - - - - 0.6986 *** 0.6362 0.761 Change in accessibility by transit 7.67 * 1.276 14.065 -2.5523 ** -4.2517 -0.8529 Change in accessibility by transit • Median household income in 2001

-0.099 * -0.181 -0.016 0.0327 * 0.0108 0.0546

Change in accessibility by car 3.37 *** 1.49 5.249 -0.5402 ** -1.0368 -0.0436 Change in percentage of residents with a bachelor’s degree or higher

0.664 *** 0.554 0.775 -0.093 *** -0.1232 -0.0627

Percentage of residents that have moved between 2006 and 2011

-0.154 *** -0.206 -0.103 0.0116 -0.0020 0.0252

Adjusted R2 0.8695 0.352

Dependent Variables: Median household income in 2011 ($1,000), Unemployment rate in 2011 (%) * 95% significance level | ** 99% significance level | *** 99.9% significance level † 95% confidence interval

35

TABLE 4 - Predicted 2011 income and unemployment rates for each income decile in 2001

Change in transit accessibility = 0 Change in transit accessibility = 1

Income

decile

Income

2001

Unemployment

rate 2001

Predicted

income 2011

Predicted

unemployment rate

2011

Predicted

income 2011

Predicted

unemployment rate

2011

1 38,967 9.7260 47,100 11.4435 50,913 10.1655

2 45,353 7.5418 54,260 9.9177 57,440 8.8484

3 50,835 6.5180 60,404 9.2024 63,042 8.3124

4 57,487 5.8651 67,860 8.7463 69,839 8.0738

5 63,125 5.6117 74,182 8.5693 75,603 8.0812

6 70,204 5.0530 82,117 8.1790 82,837 7.9223

7 75,605 4.6826 88,172 7.9202 88,357 7.8402

8 81,954 4.6638 95,289 7.9071 94,846 8.0347

9 89,749 4.1651 104,026 7.5587 102,811 7.9411

10 216,308 4.0577 245,900 7.4837 232,155 12.0046

4. CONCLUSION

Accessibility to jobs by public transport is a key factor explaining the quality of life of individuals.

Results show that accessibility to jobs by public transport is relatively vertically equitable in the

Greater Toronto and Hamilton Area, although vertical equity decreased between 2001 and 2011.

The census tracts with the lowest income boast the highest accessibility to jobs thanks to their

proximity to downtown Toronto and the public transport network, while wealthier groups

experience lower accessibility levels.

This study suggests that, for low and medium income census tracts, increases in transit

accessibility are related to higher increases in income. For wealthier census tracts, increases in

transit accessibility are associated with decreases in income, potentially due to the migration of

high-income populations to less dense neighbourhoods, away from transit. The change in

accessibility by car, on the other hand, has a uniform effect across income deciles and is

associated with larger income increases. The equity of accessibility to employment opportunities

thus plays a key role in determining resulting equity of outcome, stressing the need for methods

that can incorporate equity considerations into the evaluation of new transportation projects.

It is important to note that the findings from this study are not conclusive, nor can they determine

a causal relationship; more analysis is needed in multiple cities across the globe to further

investigate the relationship between accessibility improvements and changes in income and

unemployment. While multiple variables related to migration were examined, this study does not

36

fully capture the impacts of population movement between 2001 and 2011. The uncovered

relationship could therefore potentially be explained by transit accessibility attracting medium

income populations, resulting in increases in income for low income areas, and decreases in

income for the wealthiest neighbourhoods. This highlights the need for further research in order

to disentangle the complex socio-spatial relationships uncovered in this study. Ideally, future

research should employ micro-data to track individuals over time, and use surveys and interviews

to shed more light on individual changes in accessibility and socio-economic status.

Future studies should also include the cost of transportation in their analysis and normalize the

fares according to income. This would lower the accessibility of the entire population (El-Geneidy,

Levinson, et al., 2016), and could reduce accessibility for socially vulnerable groups compared to

wealthier groups.

Different types of jobs were not distinguished in the present study, although people cannot access

all the different jobs that exist within a city; an individual without a high school diploma will not be

able to access the high-wage service-sector jobs that cities offer, regardless of the transport and

land use system. Future studies should therefore differentiate low, medium, and high income jobs

when comparing accessibility across different groups and different years. The analysis should

also take into account the time when different jobs start and incorporate the time aspect in the

calculation of accessibility by public transport.

Nevertheless, the results of this study demonstrate a clear association between improvements in

accessibility by transit and positive outcomes (measured by changes in income and

unemployment) for neighbourhoods with low and medium income. The relationship uncovered in

this study establishes new directions for future research in order to explore the equity of outcome

resulting from changing accessibility levels.

ACKNOWLEDGEMENTS

This research was funded by the Social Sciences and Humanities Research Council of Canada

(SSHRC) program. We would like to thank David King, Nicholas Day and Joshua Engel-Yan from

Metrolinx for providing the car travel time matrix in 2011. The authors would also like to thank

David Levinson and Emily Grisé for their helpful insights.

37

CHAPTER 3: ACCESSIBILITY-ORIENTED DEVELOPMENT3

Local authorities worldwide continue to pursue transit-oriented development (TOD) strategies in

order to increase transit ridership, curb traffic congestion, and rejuvenate urban neighbourhoods

(Cervero et al., 2002; Curtis et al., 2009; Papa & Bertolini, 2015; Ratner & Goetz, 2013). For years,

TOD has garnered attention by scholars and transport professionals alike (Calthorpe, 1993; City

of Denver, 2014; Gilat & Sussman, 2003). Neighbourhoods are often defined as TODs when they

are situated close to transit, allow for higher density development, and possess diversified land

uses (Cervero et al., 2004; Kamruzzaman et al., 2015). TOD therefore not only involves the

construction of public transport infrastructure and provision of service, but also requires the

integration of transport and land use (Bertolini et al., 2012; Jacobson & Forsyth, 2008); in this way,

TOD intends to achieve a holistic way of compact urban development, enabled by supporting

public sector policies such as zoning and tax incentives. As TODs usually also encompass

increased attention to urban design, livable spaces and walkability, the demand for housing in

TOD areas results in increased premiums for homes located in TODs (Duncan, 2011; Mathur &

Ferrell, 2013; Renne, 2009). Residents in these areas have also been found to rely more on transit

and active modes of transport, seemingly fulfilling the promises of TOD (Chatman, 2006;

Kamruzzaman et al., 2015), although the relationship between TOD and transit use has been

found to differ between trip motives (Langlois et al., 2015), and not the ‘T’ in TOD, but rather limited

parking availability and higher density may be causing the observed decrease in car use

(Chatman, 2013).

Areas planned as TOD do not always function as foreseen; in many cities, development on

planned sites has been close to non-existent. One potential reason is that the connection between

the (planned) transit investment and land use at both the local and broader spatial scale are often

3 Co-authored with Ahmed-El Geneidy and David Levinson. Paper presented at the 2018 Annual TRB Meeting, Washington DC. This chapter is currently under review as a manuscript at

Transport Geography

38

overlooked. At the local scale, transit-adjacent developments (TADs) may fail to take advantage

of their proximity to transit and bring almost none of the benefits normally associated with TODs

(Renne, 2009). The often physical nature of the definition of TODs (‘density near transit’)

contributes to this problem (Belzer & Autler, 2002).

However, it is at the regional scale that the TOD concept tends to break down more often. TOD is

an inherently local planning tool, and does, at its core, not consider regional land use patterns.

While regional approaches to TOD planning have been proposed (see e.g. P. Newman (2009);

Staricco and Vitale Brovarone (2018)), they are not sufficient to combat this issue and their use is

far from widespread. The TOD concept, even in its regional variant, only considers access to

transit, but not the accessibility that is provided by transit (i.e., what destinations does the transit

service allow me to access?) (Belzer & Autler, 2002; Guthrie & Fan, 2016; Renne, 2009). As travel

patterns are mostly determined by the region-wide levels of accessibility provided by transport

systems, the use of TOD, as such, is insufficient to increase transit usage (Boarnet, 2011;

Chatman, 2013) and to attract urban development. We contend that these issues can be alleviated

by introducing the concept of accessibility-oriented development (AOD).

AOD will help planners to explicitly consider not just access to transit, but also the accessibility

provided by transit. Accessibility, or the ease of reaching destinations, is an easy-to-use measure

that can help unravel the intricacies involved in combined land use and transport planning in the

minds of planning professionals and urban decision makers (Boisjoly & El-Geneidy, 2017a).

Access to destinations is usually operationalized as the number of destinations that can be

reached from a certain point in space. As such, accessibility recognizes the inherent connection

between transport and regional land uses (in the form of destinations) and can be used to

overcome the local focus of TODs.

We define accessibility-oriented development as a strategy that balances accessibility between

employment opportunities and workers to foster an environment conducive to urban development.

AOD occurs both naturally through the market and with direction from planners. The AOD concept

invites planners to leverage access to steer, slow down, or speed up the phenomena that naturally

follow from accessibility changes, namely changing commute times and economic development.

AOD areas are therefore neighbourhoods or sites where planners are using the various tools at

their disposal to control accessibility levels in order to attract a particular mix of residential,

commercial or industrial development. We hypothesize that transport investments made on the

principles of AOD planning will naturally result in development occurring in the targeted

39

neighbourhoods, and, through lower commute times, a better quality of life for residents. This

study aims to test the hypotheses underlying the accessibility-oriented development concept.

The rest of this chapter is organised as follows. Section 2 describes the concept of accessibility

and links it with economic development. Section 3 defines AOD more thoroughly and assesses

the validity of using AOD, by testing the two underlying hypotheses in a case study of the Greater

Toronto and Hamilton Area, Canada, using access to jobs and workers in 2001 and 2011. In

Section 4 the results of the regression models testing AOD are discussed. Section 5 then

concludes the chapter and provides policy recommendations for the implementation of AOD.

1. LITERATURE

1.1 Accessibility

Accessibility is a comprehensive measure of the land use and transport interaction in a region and

illustrates the ease of reaching destinations (Geurs & van Wee, 2004; Handy & Niemeier, 1997).

Accessibility was first defined by Hansen (1959), who used the measure to develop a residential

land use model, under the assumption that accessibility was a main driver of residential

development. This paper builds on this seminal work by testing the relationship between

accessibility and development across different modes in the Greater Toronto and Hamilton Area.

Two measures of accessibility are widely employed. Cumulative opportunity measures of

accessibility compute how many opportunities an individual can reach within a predefined time

threshold (Wickstrom, 1971), whereas gravity-based (or, equivalently, time-weighted cumulative

opportunity) accessibility measures relax the assumption that people only travel until an arbitrary

threshold, and discount opportunities by distance (or time) (Hansen, 1959). While gravity-based

measures of accessibility more realistically reflect behavior, they require the prediction of a

distance decay function and are thus more difficult to calculate, communicate, and compare

across studies (El-Geneidy & Levinson, 2006).The concept of accessibility has been widely used

to shed light on the benefits resulting from land use and transport systems. These benefits range

from higher land values (El-Geneidy, van Lierop, et al., 2016), over smaller risks of social exclusion

(Lucas, 2012), to shorter unemployment duration (Andersson et al., 2014; Korsu & Wenglenski,

2010) and increased odds of firm birth in areas with high accessibility levels (Holl, 2004).

Furthermore, access by public transport has been shown to be related to increased transit mode

share (A. Owen & Levinson, 2015a). Accordingly, to measure how these benefits are distributed

across different socio-economic groups, accessibility measures have also been used to examine

the equity of the transport and land use interaction (Bocarejo & Oviedo, 2012; Delmelle & Casas,

40

2012; Foth et al., 2013; Golub & Martens, 2014; Guzman et al., 2017). However, even though the

connection between transport and economic development has been extensively investigated,

insufficient research has coupled comprehensive measures of accessibility with urban

development.

Accessibility is increasingly being incorporated into metropolitan transport plans and national

planning guidelines, although mobility-planning remains the dominant paradigm (Boisjoly & El-

Geneidy, 2017c; D. Proffitt et al., 2017). In the United Kingdom, a national accessibility framework

exists, but analysis is still “generally too transport focused” and accessibility indicators are

“misused” and “abused” (COST, 2012; Halden, 2011). At the municipal or regional scale, cities

such as London, Paris, Sydney, and Atlanta are now employing the concept of accessibility, either

as an independent goal or objective, or as part of an environmental justice assessment (Boisjoly

& El-Geneidy, 2017a). In both Sydney and London, improving access to jobs or employment is

mentioned as a key method to support regional economic development, and the ‘30-minute’ city

is a key element to Sydney’s long-range plan (Greater Sydney Commission, 2018; NSW

Government, 2012; Transport for London, 2006). Canadian cities, however, have been slow to

adopt the concept; while their plans mention access to transit, only the discussion paper for the

updated “Big Move” for Toronto contains a metric for access to jobs by transit, with goals similar

to the London plan (Metrolinx, 2016). Similarly, in the United States, only a few cities have adopted

accessibility goals and performance metrics in their regional transport plans (D. Proffitt et al.,

2017). Accessibility planning practice thus remains limited across North America.

1.2 Transport, accessibility and urban development

A large body of literature has focused on establishing a theoretical framework between transport

and subsequent land use patterns and urban development. Kain (1962) and later Alonso (1964)

extended the model developed by von Thünen representing land value as a function of distance

to a central business district, and argued that land values in turn influence land use patterns. The

bid-rent theory developed by Alonso (1964), and later extended by many other scholars (see for

example Anas and Moses (1977); Mills (1967)), offers households a trade-off between transport

cost and rent, resulting in higher land values for more central locations. The area with the highest

accessibility attracts the most development and becomes the CBD. In a self-reinforcing process,

because it has greater access to both jobs and workers, competition will favor more intensive

development in this central location, and according to bid-rent theory, prices will rise. Changes in

the transport system are therefore said to result in changes in land use patterns through the

intermediating effect of commute duration and land values.

41

In a similar vein as the urban economics scholars before them, transport researchers focusing on

accessibility have linked transport changes to changing land use and activity patterns (El-Geneidy

& Levinson, 2006; Forkenbrock et al., 2001; Giuliano, 2004). Many governments and transit

agencies have also acknowledged the link between transport and economic development

(European Commision, 2010), and many cities and regions worldwide are looking to capitalize on

this link through land value capture (Medda, 2012; Salon & Shewmake, 2014; Smolka, 2013;

Transport for London, 2017; Zhao et al., 2012).

Public sector policy, and economic and population growth, play vital roles in determining the

viability of the links presented above (Giuliano, 2004; Warade, 2007). Supporting tax and land use

policies, for example, can expedite how changes in accessibility impact land use, while the general

economic climate is a vital aspect in determining whether or not development will occur on the

site. Banister and Berechman (2000) argue that coordination between regional and municipal

agencies, combined with favorable economic circumstances, are pre-conditions for the

association between transport and development to occur.

The links presented above have subsequently been investigated in a myriad of empirical studies.

Levinson (1998) examines the association between accessibility measures and commute

duration. In a cross-sectional study, he finds that, for origins, access to employment opportunities

is inversely related to average commute duration, while access to housing is positively correlated

to average commute time. The association between accessibility and land values is considered

by El-Geneidy, van Lierop, et al. (2016), Franklin and Waddell (2003) and Martínez and Viegas

(2009), among others, who find that higher accessibility levels are related to increased home

values. Iacono and Levinson (2015), on the other hand, conclude that, although homes in

neighbourhoods with higher accessibility levels command value premiums, the relationship no

longer holds for changes in accessibility. Maturity of the transport network is said to be causing

this effect. Similarly, Du and Mulley (2006) find that the effects of accessibility on home values

depend on location and the accessibility level of the neighbourhood.

The relationship between transport investments and economic benefits is assessed by Banister

and Berechman (2000), Mejia-Dorantes et al. (2012), and Padeiro (2013), among others. They

find that transport infrastructure changes are related to economic development, although the

relationship varies by location and occurs mostly in sectors showing large agglomeration

economies, such as finance and real estate. Mejia-Dorantes et al. (2012) show that distance to

subway stations is a key determinant of firm location, while Padeiro (2013), in a case study of

small municipalities in the Île-de-France region, concludes that the presence of train stations does

42

not significantly affect job growth, whereas the presence of a highway is only a significant predictor

of growth for the smallest municipalities.

Ozbay et al. (2003) investigate the relationship between accessibility and economic development

in the New York – New Jersey region and find that accessibility changes are related to changes

in employment growth (and therefore land use). Similarly, Alstadt et al. (2012) find that local

accessibility calculated at a 40-minute threshold is a strong factor impacting economic activity in

the service sector, while regional accessibility computed with a 3-hour threshold is more valued

by the manufacturing sector. In a case study of motorways in Portugal, Holl (2004) develops a

measure of market access similar to a gravity-based measure of access to the labour force, and

concludes that the odds of firm birth are higher for several manufacturing and construction sectors

when market access is larger. Applied to a case study in Chicago, Warade (2007) develops a

quasi-integrated land use and transport model and concludes that higher access to jobs is

associated with increased household density, whereas higher access to workers is related to

increased job density. Y. Shen et al. (2014) examine the effects of local and regional accessibility

on development near the Atocha station in Madrid, Spain. The authors find that accessibility, at

both the city and country level, is a significant predictor in determining land cover change.

Farber and Grandez Marino (2017) acknowledge the strong association between accessibility and

urban development, and generate a typology of planned stations in the Greater Toronto and

Hamilton Area based on development potential around the station and the projected change in

accessibility. The authors conclude that there exists considerable mismatch between development

potential and large predicted accessibility changes from planned stations. This conclusion

highlights the need for accessibility considerations when investing considerable amounts in new

transport infrastructure, in order to realize the full benefits of the planned investment. We contend

that the introduction of AOD can greatly benefit this process.

2. HYPOTHESES, DATA, AND METHODOLOGY

2.1 Hypotheses

Based on the theoretical accessibility and development framework and the empirical literature

presented above, we hypothesise that accessibility drives the following natural phenomena:

Hypothesis 1: Residents in neighbourhoods with high access to employment opportunities and

low access to workers experience the shortest average commute duration, and vice versa.

43

Hypothesis 2: Neighbourhoods with high accessibility levels to both employment opportunities and

workers attract more development.

• Hypothesis 2a: High access to workers draws in more businesses.

• Hypothesis 2b: High access to employment opportunities invites residential and

commercial development by influencing home location choice and leveraging

agglomeration economies.

TABLE 5 - Summary of AOD hypotheses

Areas with: High Access to Jobs Low Access to Jobs

High Access to Labour [Urban centre]

H2a: Attracts employment and residences (commercial and residential development)

H1a: Residents have long commutes

H2a: Attracts employment (commercial development)

Low Access to Labour H1b: Residents have short commutes

H2b: Attracts residences (residential development)

[Urban fringe]

Accessibility-oriented development invites planners to leverage access to steer, slow down, or

speed up the phenomena that naturally follow from accessibility changes as presented in the

hypotheses above. We therefore define accessibility-oriented development as a strategy that

balances accessibility between employment opportunities and workers in order to foster an

environment conducive to development. This differs from the traditional ‘jobs-housing balance’

literature by avoiding the use of arbitrary municipal boundaries and instead considers the

relationship between access to jobs and access to competing workers (Cervero, 1989, 1996;

Levinson, 1998; Levinson et al., 2017).

AOD will allow planners to leverage tools such as zoning measures to attract desired development

into TOD sites. For example, assume that an area has been designated as a TOD site in planning

documents. The zone can then become more attractive for commercial/industrial development –

if the hypotheses above hold – by increasing access to the labour force, which can be achieved

by (1) zoning a part of the proposed TOD as residential, to provide a direct customer/labour base,

and (2) offering new and improving current transport options to a variety of residential

neighbourhoods. AOD thus asks planners to leverage accessibility levels (in this case, increasing

44

access to workers) to attract desired development (commercial and industrial in the case of the

example).

In short, employing AOD in focus areas by increasing job accessibility should help shorten

commute times and attract residents to these neighbourhoods, helping these regions to

rejuvenate. Other areas could be designed to allow for maximum access to the labour force, which

would provide incentives for firms in the service and retail sectors to locate themselves in these

neighbourhoods, in order to minimize their employees’ or customers’ travel times and benefit from

agglomeration economies. This in turn would lower commute times. Development would therefore

occur naturally in sectors planned with AOD principles, once the starting conditions are set by

adequate policy.

Ideally, TOD can be understood as a component of AOD. Whereas TOD solely focuses on local

access/egress conditions to public transport and the local distribution of land use, AOD also

considers access by public transport and the regional distribution of land use, as well as access

to destinations by other modes.

2.2 Case study

To test the hypotheses about the phenomena that naturally follow from accessibility levels and

thereby the validity of using an AOD approach, a case study is performed in the Greater Toronto

and Hamilton Area, Canada (GTHA) between 2001 and 2011. The GTHA is the largest

metropolitan agglomeration in Canada, housing 6.6 million residents in 2011 and comprises the

Hamilton, Toronto and Oshawa census metropolitan areas (CMAs). Population in the region

increased by over 1 million inhabitants during the study period, while the total number of jobs grew

from 2.9 to 3.5 million (Statistics Canada, 2015). Between 2001 and 2011, the transport network

in the region underwent substantial changes: a new subway line was opened in 2002, and several

new train stations were constructed. A context map of the GTHA can be seen in figure 10.

Note that the choice of this particular case study is largely irrelevant in providing the foundations

for an AOD approach. The case study only serves to corroborate that the two hypotheses about

accessibility hold, even in a region where accessibility-oriented development tactics have not been

employed. If, and only if, the hypotheses hold, AOD will be a valid strategy to develop sites or

neighbourhoods.

45

FIGURE 10 - Context map of the Greater Toronto and Hamilton Area

2.3 Data and Methods

To generate cumulative accessibility measures by both car and public transport (PT), data from

Metrolinx and Statistics Canada were used. The cumulative opportunities metric was chosen

because it is easier to communicate to planners and urban decision-makers, increasing its

chances to impact policy (Geurs & van Wee, 2004; Handy & Niemeier, 1997). First, travel times

between census tract centroids, modeled through the EMME software, by both car and public

transport in 2001 were acquired from Metrolinx, in addition to car travel times in 2011. Public

transport travel times in 2011 were subsequently calculated by making use of data from the

General Transit Feed Specification (GTFS). A network was built based on this data in ArcGIS

through the tool ‘Add GTFS to a network dataset’, and fastest routes were calculated between

each census tract centroid on a regular Tuesday at 8 AM in 2011. The fastest path algorithm took

into account walking time, waiting time, and in-vehicle time (as determined by the transit

46

schedule), but did not assign a penalty for transfers. Census data provides the number of jobs and

the population in the labour force in each census tract.

To reflect the commuting behavior of an average individual, car accessibility was calculated for a

time threshold of 30 minutes (equivalent to the average commute duration in the region by car),

while access by transit was computed for a 45 minute time threshold (equivalent to the average

commute duration in the region by public transport (Statistics Canada, 2010)). A cumulative

measure of accessibility then counts the number of opportunities that can be reached within that

threshold. As the data sources for the number of jobs differed between 2001 and 2011, a relative

measure of accessibility was calculated by dividing the total number of jobs (workers) reachable

within the threshold by the total number of jobs (workers) in the region. Accessibility can then be

interpreted as the percentage of all jobs (workers) in the region an individual can access: a value

of 1 signifies that all jobs (workers) can be reached within the threshold (30 or 45 minutes

depending on the mode), while a value of 0.25 indicates that 25% of all jobs (workers) can be

reached within the set time frame. The accessibility calculations were performed for each census

tract in the GTHA.

To test the two AOD hypotheses, commute duration for 2011 was gathered from Statistics

Canada, while commute duration in 2001 was calculated based on travel times and origin-

destination flows provided by Statistics Canada. Development was subsequently measured by the

change in the percentage of open area in the census tract (measured as the area not used for

residential, commercial, industrial, governmental, or park purposes). The AOD hypotheses were

then examined through five regression models, relating commute duration, open area, and job

and population density with accessibility and accessibility changes.

A first, cross-sectional, model predicts average commute duration in 2001 based on accessibility

in 2001 and a dummy variable for the Hamilton CMA. A dummy variable for Hamilton was

introduced to reflect that residents of census tracts in the Hamilton CMA are more likely to

commute to Hamilton than Toronto, thus their commute time is, on average, lower than in the

Toronto or Oshawa census metropolitan areas.

A second model, to test if the relationship between commute duration and accessibility also holds

over time, predicts commute time in 2011 based on observed commute times in 2001 (by car and

public transport combined) and accessibility in 2001, as well as changes in accessibility levels

between 2001 and 2011. Levels of accessibility in 2001 were included as it is assumed that the

initial situation will influence how changes occur (Putnam, 1983). Model 1 and 2 together thus

47

examine the link between accessibility and commuting behavior, in order to validate our first AOD

hypothesis, namely that inhabitants of AOD areas with high access to jobs and low access to the

labour force experience the lowest commute times.

A third model was developed to assess the second AOD hypothesis, with open area

acting as a proxy for development. Open area was measured as the area not used for residential,

commercial, industrial, governmental, or park purposes. The same model specification as the

second model is used: open area in 2011 is predicted based on open area and accessibility in

2001, and changes in accessibility between the two years. In order to disentangle the separate

effects of labour and employment accessibility on attracting residential, commercial, and industrial

development, two extra regressions were performed: one predicting job density and the other

predicting population density in 2011. Descriptive statistics of the variables used in the different

models are shown in Table 6.

TABLE 6 - Descriptive statistics for commute duration, accessibility and development data

Variable Description Mean Standard dev.

Commute01 Average commute time in 2001 (min) (all modes) 28.58 6.55 Commute11 Average commute time in 2011 (min) (all modes) 31.29 4.25 Access01 to Jobs by Car Access to jobs by car in 30 minutes in 2001 (%) 20.12 12.25 Access01 to Workers by Car Access to workers by car in 30 minutes in 2001 (%) 20.75 6.86 Access01 to Jobs by PT Access to jobs by PT in 45 minutes in 2001 (%) 8.90 9.97 Access01 to Workers by PT Access to workers by PT in 45 minutes in 2001 (%) 7.53 6.79 Access11 to Jobs by Car Access to jobs by car in 30 minutes in 2011 (%) 30.97 20.24 Access11 to Workers by Car Access to workers by car in 30 minutes in 2011 (%) 29.26 10.36 Access11 to Jobs by PT Access to jobs by PT in 45 minutes in 2011 (%) 8.17 8.62 Access11 to Workers by PT Access to workers by PT in 45 minutes in 2011 (%) 6.32 5.20 ∆ Commute Change in commute time (min) (all modes) 2.71 4.70

∆ Access to Jobs Car Change in access to jobs by car (%) 10.84 10.17

∆ Access to Workers by Car Change in access to workers by car (%) 16.74 7.59 ∆ Access to Jobs by PT Change in access to jobs by PT (%) -0.73 3.34 ∆ Access to Workers by PT Change in access to workers by PT (%) -1.21 2.67 OpenArea01 Percentage of open area in 2001 (%) 14.61 15.92 OpenArea11 Percentage of open area in 2011 (%) 14.57 24.13 JobDens01 Job density in 2001 (jobs/km2) 181.45 526.07 JobDens11 Job density in 2011 (jobs/km2) 164.64 544.94 PopDens01 Population density in 2001 (population/km2) 4337.34 4781.05 PopDens11 Population density in 2011 (population/km2) 4903.45 5285.02

48

3. RESULTS AND DISCUSSION

Figure 11 shows normalized accessibility levels by car to employment opportunities and workers.

Access to jobs by car is highest in downtown Toronto, while the highest accessibility levels to

workers are present in neighbourhoods that form a ring around the Toronto CBD. This reflects that

the central business district houses fewer people than the area immediately surrounding it, and

that it is easier for residents of the outskirts of the region to travel to these suburban locations than

to the city centre. Between 2001 and 2011, access to workers increased substantially more across

the study area than access to jobs. According to the second AOD hypothesis, the suburban

locations with high access to the labour force should experience more job creation during the

study period, providing that the benefits of access to labour outweigh those of existing

agglomeration economies of access to existing businesses (operationalized as access to jobs).

Accessibility levels by public transport are shown in figure 12. Access by transit is considerably

lower than access by car, even with an extra 15 minutes of travel time, in both years, for access

to jobs and workers. High access by transit is mainly present in downtown Toronto and in areas

located in close proximity to the commuter rail lines. Unlike the spatial patterns present in access

by car, the two accessibility measures for public transport, to jobs and workers, are highly

correlated (a correlation of 0.95). In 2011, suburban areas located next to the public transport

network have seen increases in accessibility, while areas with traditionally high access (such as

downtown Toronto) have seen a small decrease in access, which might be related to an ongoing

suburbanization of jobs, combined with investments made in the commuter train network during

the study period.

49

FIGURE 11 - Access to jobs and the labour force by car within 30 minutes

50

FIGURE 12 - Access to jobs and the labour force by public transport within 45 minutes

3.1 Accessibility, commute duration and urban development

The results of the model associating average commute duration in 2001 and accessibility (Model

1) are shown in Table 7. Note that access by public transport was not included in this model due

to collinearity with access by car. A separate model was tested for public transport accessibility

and resulted in similar conclusions, but was excluded from the analysis due to its similarity with

the reported model.

Higher access to jobs is related to shorter commute times, ceteris paribus, while a higher access

to the labour force is related to longer commute times, all else equal, which is consistent with the

51

findings from Levinson (1998). In absolute terms, an extra 100,000 accessible jobs decreases

commute time by 0.48 minutes (-0.14 minutes per percent), while an extra 100,000 workers

increases average commute duration by 0.87 minutes (0.27 minutes per percent). These results

corroborate the first AOD hypothesis: AOD areas with high access to jobs and low access to the

labour force have shorter average commute times than the rest of the region (and vice versa).

The dummy variable for Hamilton indicates that, all else equal, commute time in the Hamilton

census metropolitan area is 6.4 minutes shorter. Note that accessibility levels also influence the

predicted commute duration in Hamilton. Evaluated at the average accessibility levels for Hamilton

(9% of all jobs accessible by car and 12% of all workers accessible by car), census tracts in

Hamilton have an average predicted commute duration of 22.2 minutes, 7.2 minutes less than the

predicted average for the entire study area.

TABLE 7 - Regression model predicting average commute duration in 2001

Variable Coefficient Sig. Confidence int.† Hypothesis

Intercept 26.5799 *** [25.3558, 27.8040]

Access01 to Jobs by Car -0.1411 *** [-0.1699, -0.1124] -

Access01 to Workers by Car 0.2722 *** [0.2178, 0.3265] +

Hamilton dummy -6.3950 *** [-7.4930, -5.2971] -

Adjusted R2 0.2212

Dependent Variable: Average commute duration in 2001 * 95% significance level | ** 99% significance level | *** 99.9% significance level † 95% confidence interval

The results of the temporal model relating commute time and accessibility (Model 2) are shown in

Table 8. Almost 60% of the total variation in commute times in 2011 is explained by this model.

The coefficients for accessibility in 2001 have the expected signs and statistical significance:

access to jobs in 2001 is associated with a shorter commute time, while access to the labour force

is related to a longer commute duration. The statistical significance of both coefficients could be

related to a time lag between accessibility levels and commute patterns adjusting themselves to

the new situation, i.e., commute patterns in 2001 were not yet in equilibrium with respect to 2001

accessibility.

Unlike in the cross-sectional model, the effects of both changes in access by public transport and

car can be investigated separately, as their changes are no longer correlated. Notably, only

changes in access to workers by car and access to jobs by public transport are statistically

significant predictors of average commute duration in 2011. These results confirm that the first

AOD hypothesis also holds over time. An increase in the change in accessibility of 1% of all

52

workers by car lengthens commutes by 0.2 minutes, while a 1% increase in access by public

transport to all jobs reduces commutes by 0.1 minutes. Interestingly, the relative magnitudes of

both coefficients are reversed compared to the cross-sectional model.

The two coefficients for change in access to jobs by car and to workers by public transport were

found to be not statistically significant. We hypothesize that this is related to the maturity of the

transport network in the region. Small changes to the network can no longer induce large impacts

on accessibility levels (Gómez-Ibáñez, 1985), resulting in diminishing returns to the outcomes of

transport investments (Iacono & Levinson, 2015).

TABLE 8 - Commute time and open area in 2011 fitted to accessibility in 2001 and changes in accessibility between 2001 and 2011

Commute duration in 2011 Open area in 2011

Variable Coeff. Sig Confidence int. † Hyp. Coeff. Sig Confidence int. † Hyp.

Intercept 17.8976 *** [17.0579, 18.7372] 21.3201 *** [17.9573, 24.6829] Access01 to Jobs by Car -0.1027 *** [-0.1250, -0.0804] - -0.1948 ** [-0.3185, -0.0710] - Access01 to Workers by Car 0.0793 *** [0.04652, 0.1122] + -0.6212 *** [-0.8162, -0.4262] - ∆ Access to Jobs Car -0.0093 [-0.0306, 0.0121] - 0.0251 [-0.0989, 0.1490] - ∆ Access to Workers by Car 0.2139 *** [0.1745, 0.2532] + -0.0595 [-0.2875, 0.1685] - ∆ Access to Jobs by PT -0.1083 *** [-0.1713, -0.0453] - 0.2876 [-0.0810, 0.6561] - ∆ Access to Workers by PT -0.0652 [-0.1558, 0.0254] + -0.6645 * [-1.1883, -0.14075] - Commute01 (Observed) 0.3561 *** [0.3295, 0.3826] + OpenArea01 0.4958 *** [0.4633, 0.5282] +

Adjusted R2 0.5922 0.5459 Dependent Variables: Average commute duration and open area in 2011 * 95% significance level | ** 99% significance level | *** 99.9% significance level † 95% confidence interval The results for the model predicting the percentage of open area in each census tract in 2011

(Model 3) can be seen in Table 8, explaining 55% of all variation in open space. The statistically

significant coefficients for accessibility in 2001 corroborate the second AOD hypothesis: access

to jobs and workers in 2001 are associated with decreases in open area. One extra percent of

access to jobs by car in 2001 reduces open space by 0.19%, and an extra percent of access to

workers decreases open space 0.62%. Residential, commercial, and industrial development thus

seems to be associated with AOD areas.

Changes in accessibility levels, except for the change in worker access by public transport, are

not statistically significant predictors of open space in 2011. Two explanations are possible. First,

location choices do not occur often due to the associated capital costs, thus there exists a

substantial time lag between accessibility levels changing and location choice. A study period

encompassing only 10 years will therefore not be able to fully capture these long-term decisions,

especially as it is unknown when each accessibility change occurred. It is also expected that firms,

53

rather than individuals, are more sensitive to changes in access to jobs and workers, and are more

prone to change their locations (as residents also place high value on access to other

opportunities, such as schools, shops, and social networks). The statistically significant coefficient

for the change in access to workers by transit corroborates this, as it is expected that access to

workers is an attractor in firm location behavior. As only the change in access to workers by public

transport is statistically significant, we can conclude that firms in the GTHA are more likely to

locate near areas where transit service, instead of car accessibility, increases. Although this

relationship might depend on the business sector and their associated transport costs for their

products and employees, it could be indicative of a paradigm shift in the way (some) enterprises

expect their employees or customers to travel. Second, as some areas are almost fully built,

changes in accessibility in these neighbourhoods can no longer reduce open space and can

therefore not be captured by the model.

To resolve this second possibility, and to confirm the hypotheses about firm and individual

behavior mentioned above (hypotheses 2a and 2b), two extra models were computed, predicting

job and population density in 2011, see Table 9. These models again confirm the second AOD

hypothesis: job density increased more in areas where baseline access to workers was highest,

whereas population density grew considerably more in areas where 2001 access to jobs was

highest. This corroborates the hypothesis that firms are attracted to where workers and customers

are located, whereas individuals are more likely to choose a home with high access to job

opportunities.

Surprisingly, access to jobs in 2001 is not significant in the model predicting job density and has

a negative coefficient, indicating that businesses were more likely to locate away from existing

jobs. When a squared term of this variable is added to the model, the relationship follows a more

intuitive pattern, although it is still insignificant: once there is critical mass of job accessibility,

businesses are attracted to job-rich areas, corroborating the importance of agglomeration

economies. Furthermore, this research uses a crude measure of ‘jobs’, implicitly assuming all jobs

are equivalent. While some jobs are in agglomeration-favoring industries (like finance and

government), others need to be near, but not central to, agglomerations (like industry), or near

customers (retail, services).

Among the change variables, only the change in access to the labour force by transit is statistically

significant: a 1 percent increase in access to workers by public transport between 2001 and 2011

is associated with an extra 8 jobs per square kilometer. As with the model predicting open area, it

54

appears that firms in the region were more likely to be attracted to areas where the public transport

system, instead of the highway and street network, improved.

The model predicting population density in 2011 shows that all changes in accessibility, except for

the change in access to jobs by transit, are statistically significant. The two coefficients for the

change in access to the labour force by car and transit are positive, suggesting that individuals

are attracted to locations where worker accessibility increased during the study period. Note that

the significance of these variables could be related to reverse causality: as job-rich areas attract

more residents, worker accessibility will necessarily increase in and surrounding these areas. The

change in population density in the neighbourhood and in surrounding census tracts might

therefore cause the change in worker access. The coefficient for the change in access to jobs by

car is negative and thus contradicts our hypothesis: an increase in job accessibility of 1% between

2001 and 2011 is related to a decrease in population density of 23 inhabitants per km2. This might,

however, indicate a trade-off between residential and commercial development in a census tract,

or might be related to larger scale zoning patterns that do not allow concurrent residential and

commercial development in a single zone. Nevertheless, the significance and signs of the majority

of the variables in the model corroborate the second hypothesis.

TABLE 9 - Job and population density in 2011 fitted to accessibility in 2001 and changes in

accessibility between 2001 and 2011

Job density in 2011 Population density in 2011

Variable Coeff. Sig Confidence int. † Hyp. Coeff. Sig Confidence int. † Hyp.

Intercept -31.9827 * [-62.2608, -1.7045] 81.3165 [-379.6737, 542.3068] Access01 to Jobs by Car -1.2361 [-2.5445, 0.0724] 21.3944 * [ 1 .9579 , 40 .8308 ] + Access01 to Workers by Car 2.1506 * [0.1992, 4.1020] + -21.9910 [-51.3465, 7.3645] ∆ Access to Jobs Car 0.4887 [-0.7560, 1.7333] -23.0279 * [-41.8525, -4.2032] + ∆ Access to Workers by Car -0.0960 [-2.3794, 2.1874] + 67.2478 *** [32.6678, 101.8279] ∆ Access to Jobs by PT -1.6380 [-5.3603, 2.0844] -37.1115 [-93.4974, 19.2743] + ∆ Access to Workers by PT 8.4400 ** [3.2029, 13.6772] + 174.8577 *** [95.6686, 254.0467] JobDensity01 1.0042 *** [0.9853, 1.0231] + PopDensity01 0.9584 *** [ 0 . 9 2 5 9 , 0 . 9 9 0 9 ] +

Adjusted R2 0.931 0.787

Dependent Variables: Average commute duration and open area in 2011 * 95% significance level | ** 99% significance level | *** 99.9% significance level † 95% confidence interval

4. CONCLUSION

Accessibility-oriented development, both a market process and a strategy that aims to balance

accessibility between employment opportunities and workers in order to foster an environment

conducive to development, has been shown to be associated with changing commute times and

55

economic development. Through AOD planners can leverage the relationship between transport

and land use patterns by explicitly considering the functional connections between local and

regional transport investments and local and regional land use. In contrast with TOD, AOD also

considers the access that is provided by transit, instead of only examining access to transit.

The regression models in our study indicate that, through AOD, planners can capitalize on two

tangible benefits that accrue to neighbourhoods when accessibility levels change. First, by

influencing access to jobs and/or workers through land use (e.g. zoning) or transport changes,

average commute times can be adjusted across neighbourhoods: increases in access to jobs are

related to decreases in commute duration, while increases in access to workers are associated

with longer average commute times. Second, higher access to jobs and/or workers is associated

with residential, commercial, and industrial development.

It is important to note that the relationships uncovered in this study are not conclusive, nor can

they determine a causal relationship; more studies would need to be developed in multiple cities

to further corroborate these findings. Furthermore, the analyses conducted in this study were

performed under the assumption that the land market in the GTHA operates in perfect market

conditions. Toronto, however, as with most cities in the world, regulates and prioritizes certain

land uses in their many plans and programs, potentially altering the effects of accessibility on

location choices in favor of the city’s development guidelines.

Nevertheless, this study provides strong evidence of the relationship between accessibility,

commute duration, and residential and firm location. Planners and urban decision-makers aiming

to create successful developments should therefore not limit themselves to using site-specific

planning tools such as TOD, but should also take into account AOD principles, by measuring the

impacts of new transport or land use plans in terms of access to both employment opportunities

and workers, and then leveraging these accessibility levels to generate desired development.

ACKNOWLEDGEMENTS

This research was funded by the Social Sciences and Humanities Research Council of Canada

and the Natural Sciences and Engineering Research Council of Canada. We would like to thank

David King, Nicholas Day and Joshua Engel-Yan from Metrolinx for providing travel time data.

56

CONCLUSION

Accessibility is increasingly being used in both research and practice as a key performance

indicator for integrated land use and transport planning. Many cities around the world, in their

respective transport plans, have now adopted the concept and intend to increase access to

destinations to reduce social exclusion and provide a catalyst for economic growth.

Among Canada’s eleven largest metropolitan areas, Toronto residents experience the highest

access to jobs, followed by those in Montréal, Vancouver, Calgary and Ottawa. When examining

access to low-income jobs, however, vulnerable inhabitants of Montréal are much better off than

in any other Canadian city. Overall, vulnerable individuals experience higher accessibility levels

than their fellow residents in their respective cities, indicating that accessibility is equitably

distributed in Canada’s largest metropolitan areas. However, poverty is slowly being suburbanized

as city centres and neighbourhoods next to transit hubs are increasingly being re-developed and

re-vitalized. The most vulnerable households might therefore be pressured to move to areas

where accessibility is (much) lower than in their previous locations, which could negatively affect

vertical equity in many of Canada’s large cities.

Government policies intending to increase accessibility in socially-vulnerable neighbourhoods can

bring considerable benefits to these areas. The results from chapter 2 suggest that, for low and

medium income census tracts, increases in transit accessibility are related to higher increases in

income. An improvement in accessibility to jobs by car is similarly associated with larger income

increases. Furthermore, unemployment rates rose less in areas where accessibility levels

improved. The equity of accessibility to employment opportunities thus plays a key role in

determining resulting equity of outcome, stressing the need for methods that can incorporate

equity considerations into the evaluation of new transport projects.

The results from chapter 3 suggest that accessibility improvements can also create considerable

economic benefits: increases in accessibility to jobs and/or workers in the Greater Toronto and

57

Hamilton Area were associated with more residential, commercial, and industrial development.

Moreover, accessibility levels influenced commute durations: high accessibility to jobs was related

to shorter commutes and higher accessibility to people was associated with longer commutes.

By embracing policies to increase accessibility levels, cities can thus improve quality of life for

their most vulnerable inhabitants while at the same time attracting economic development. Cities

and their transport planning agencies can therefore greatly benefit from adopting accessibility

indicators and incorporating these into their plans and policies.

58

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